Software Development Engineer , DynamoDB Index, Search and Storage

Software Development Engineer, DynamoDB Search and Indexing

Amazon DynamoDB is a serverless, distributed NoSQL database that delivers single-digit millisecond performance at virtually any scale. The DynamoDB Search and Indexing organization is building native search capabilities that allow customers to perform vector similarity search, full-text search, and hybrid search across their operational data.

We are looking for a Senior Software Development Engineer to help design and build the distributed indexing, retrieval, and ranking infrastructure behind these capabilities. You will work on problems at the intersection of databases, distributed systems, information retrieval, and machine-learning infrastructure.

The role includes designing vector and text-indexing architectures that operate across partitioned datasets; integrating approximate nearest-neighbor algorithms and inverted indexes with DynamoDB’s storage and query systems; and building hybrid retrieval that combines semantic similarity, lexical relevance, and structured filtering.

You will help determine how search indexes are created, maintained, rebuilt, rebalanced, and recovered while the underlying DynamoDB data continues to change. You will also shape query planning, distributed execution, relevance ranking, and result aggregation while delivering predictable performance across latency, throughput, recall, freshness, availability, and cost.

This is an opportunity to shape new DynamoDB capabilities from foundational architecture through customer launch and operation at DynamoDB scale.

Key job responsibilities

• Design and implement distributed vector, full-text, and hybrid-search capabilities for DynamoDB.
• Build vector-indexing and approximate nearest-neighbor retrieval mechanisms for high-dimensional embeddings.
• Develop full-text indexing and retrieval capabilities, including tokenization, text analysis, inverted indexes, relevance scoring, and phrase or term-based search.
• Build hybrid retrieval and ranking that combines vector similarity, lexical relevance, and structured attribute filters.
• Evaluate ranking and result-fusion techniques for combining semantic and lexical results, including score normalization, rank fusion, and reranking.
• Design distributed query execution, including partition selection, query fan-out, local retrieval, top-k result merging, filtering, and aggregation.
• Build index lifecycle mechanisms covering creation, backfill, incremental updates, compaction, schema or configuration changes, rebalancing, recovery, and deletion.
• Ensure that indexes remain sufficiently synchronized with changes to the underlying DynamoDB data and define clear search-freshness and consistency semantics.
• Integrate search indexes with DynamoDB’s distributed storage, partitioning, replication, and query-processing systems.
• Optimize tradeoffs among search quality, recall, relevance, query latency, ingestion throughput, index freshness, memory usage, storage amplif

About the team
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